code: def test_hific_autoencoder_collect_parameters():
      model = HiFiCAutoencoder()
  
      model_parameters, quantile_parameters = model.collect_parameters()
  
      manual_model_parameters = [
          p
          for n, p in model.named_parameters()
          if not n.endswith(".quantiles") and p.requires_grad
      ]
      manual_quantile_parameters = [
          p
          for n, p in model.named_parameters()
          if n.endswith(".quantiles") and p.requires_grad
      ]
  
      for p1, p2 in zip(model_parameters, manual_model_parameters):
          assert torch.equal(p1, p2)
  
      for p1, p2 in zip(
          quantile_parameters, manual_quantile_parameters
      ):
          assert torch.equal(p1, p2)
  
file_path: NeuralCompression/tests/models/test_hific_autoencoder.py
imports: import pytest
  import torch
  from torch import Tensor
  from neuralcompression.models import HiFiCAutoencoder
label_desc: Use image node_id 3 for calling a global function with example usage: test_hific_autoencoder_collect_parameters() without return types